Pikes Peak

Load and Manipulate Data

Pikes_Data <- read.csv("data/Pikes_Data_for_R .csv")
AFT_data <- read.csv("data/AFT_Data.csv")

#Modify the sample names, for ease of reading, where PP still stands for Pikes Peak, and they are numbered 1 - 6 with the lowest elevation sample being 1 and highest elevation sample being 6 
Pikes_Data <- Pikes_Data %>% 
    mutate (papername = 
              ifelse(Sample_Name == "PP2084", "PP1", #If True, add label PP2
                ifelse( Sample_Name == "PP2479", "PP2", #If True, add label PP2
                  ifelse (Sample_Name == "PP2907", "PP3", #If true, add label PP3
                    ifelse (Sample_Name == "PP3597", "PP4", #If true, add label PP4
                      ifelse (Sample_Name == "PP3971", "PP5", "PP6")     #if true, add label PP5, ELSE add the label PP6
                    )      
                  )
                )
              )
    )

Plots

This section makes plots for:
1. Pikes Peak Elevation - date
2. Pikes Peak Date-eU
3. Pikes Peak Elevation vs. date with each point colored by eU
4. Pikes Peak date vs. grain size
3. Pikes Peak AFT data (Kelley and Chapin, 2004)

## [1] "Excludes bottom two AFT samples at 1777m and 1866m"
## Warning: Removed 2 rows containing missing values (geom_point).

Hefty Inputs

Grains excluded from models:
* Sample: PP1, grain:Z32, rownumber: 6 - this grain has an eU of 151.5 and a date of 312.9, which is ~ 300 Ma younger than grains w/ comprable eU

grains.not.modeled <- c(6)

Pikes_Data <- Pikes_Data %>%  
  mutate( 
    Rownumber= row_number(),
    Donotuse = (Rownumber %in% grains.not.modeled),
    bindata= cut(eU, c(0,150,350,500,900,2500)) #these are my bin cutoffs
  ) 
## # A tibble: 5 x 15
##   bindata     N RawDate_mean Rawdate_15perce… Rawdate_SD CorrDate_mean
##   <fct>   <int>        <dbl>            <dbl>      <dbl>         <dbl>
## 1 (0,150]     7         546.             82.0       30.0          670.
## 2 (150,3…     6         519.             77.8       67.1          627.
## 3 (350,5…     6         295.             44.2       88.0          389.
## 4 (500,9…     7         208.             31.2       86.8          264.
## 5 (900,2…     5         120              18         36.7          141.
## # … with 9 more variables: CorrDate_15percent <dbl>, CorrDate_SD <dbl>,
## #   mean_rs <dbl>, U <dbl>, Th <dbl>, Sm <dbl>, eU <dbl>, He <dbl>, FT <dbl>

Mount Evans

Load and Manipulate Data

Plots

Hefty Outputs

Longs Peak

Load and Manipulate Data

Plots

Hefty Outputs